Explainable prediction of Parkinson’s disease in a large multimodal database
Date
2023-09-08
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Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions of people all over the world. Accurate diagnosis is important to enable prompt interventions that can improve disease prognosis. However, the heterogeneity of PD renders an accurate diagnosis challenging, especially early in the disease phase when symptoms are known to be subtle. Thus, it is imperative to obtain reliable, non-invasive, in-vivo biomarkers for PD diagnosis. Within this context, the overarching aim of this work is to develop accurate explainable deep learning models trained from a large multimodal magnetic resonance imaging (MRI) database to classify PD and healthy controls and capable of identifying structural changes associated with PD. Therefore, the objectives of this thesis are: (1) to investigate the use of T1-weighted brain MRI as a biomarker of macro-structural changes associated with PD; (2) to investigate a combination of T1-weighted and diffusion tensor MRI as a fusion of micro- and macro-structural brain morphology and its relationship with PD. To achieve these objectives, one of the largest multi-center imaging databases of over 2,000 PD and control subjects was pooled and preprocessed as the first step. Second, an explainable deep learning model was developed to accurately classify PD patients while revealing important brain regions. Third, a multimodal explainable deep learning model was trained enabling a more in-depth understanding of the interplay between the micro- and macro-structural properties of specific brain regions and the disease. The results of this work offer an important insight into structural brain changes as non-invasive, in-vivo biomarkers of PD through an in-depth analysis of associated brain regions using large multicenter data. Deep learning models are proposed to provide generalizable and robust PD classification resulting in a 0.87 and 0.89 area under the receiver operating characteristic curve for the best unimodal and multimodal approaches, respectively. Lastly, explainability methods identified brain regions in line with current knowledge of the disease providing further evidence for the clinical utility of the developed methods. This work presents relevant findings and novel methodologies that could aid improving PD diagnosis and the acceptance of computer-aided diagnosis systems targeting PD.
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Keywords
Parkinson's disease, Magnetic resonance imaging, Artificial intelligence, Machine learning, Deep learning, Computer aided diagnosis, Multimodal imaging
Citation
Camacho Camacho, M. I. (2023). Explainable prediction of Parkinson’s disease in a large multimodal database (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.